No deep sleep1/17/2024 ![]() IEEE Int Conf Bioinform Biomed (BIBM) 2017:718–723. ISSN 1984-0063, Įl-Manzalawy Y, Buxton O, Honavar V (2017) Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering. Įngle-Friedman M (2014) The effects of sleep loss on capacity and effort. Shaffer F, Ginsberg JP (2017) An overview of heart rate variability metrics and norms. IJCAI 2001 Work Empir Methods Artif Intell 3 Rish I (2001) An empirical study of the Naïve Bayes classifier. PMID: 17282250ĮL-Manzalawy Y, Buxton O, Honavar V (2017) Sleep/wake state prediction and sleep parameter estimation using unsupervised classification via clustering, pp 718–723. Īdami A, Hayes T, Pavel M, Singer C (2006) Detection and classification of movements in bed using load cells. ![]() Mendonça F, Mostafa SS, Morgado-Dias F, Ravelo-García AG, Penzel T (2019) A review of approaches for sleep quality analysis. In: 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT), pp 1–6. Satapathy SK, Ravisankar M, Logannathan D (2020) Automated sleep stage analysis and classification based on different age specified subjects from a dual-channel of EEG signal. Ian T, Dorothy B (2009) Strobe lights, pillow shakers and bed shakers as smoke alarm signals. Long X (2015) On the analysis and classification of sleep stages from cardiorespiratory activity. With the advancement in the Internet of Things world and due to the acceptance of cloud computing platforms like AWS, the bed is becoming more user-friendly. The algorithms are the bed detection algorithm, bed sleep-wake algorithm, the sleep-stage algorithm, the wake-up alarm algorithm, the surface motion, the wave algorithm, etc. The bed learns the user preferences of the sleeping pattern and adjusts itself to it. The intelligent bed captures the data like pressure, humidity, temperature and much more to make the sleep more and more user’s personal sleep assistance. People tend to use Fitbit or OURA rings to track their sleep but during the sleep also our body moves, the bed should adjust the firmness to make the sleep more comfortable. ![]() The work done during this project revolves around those algorithms and makes some further improvements to them, which directly impacts the end users. The smart bed, equipped with sophisticated hardware, is powered by some intelligent algorithms for different purposes. This paper contains the algorithms and the software which the intelligent bed manufacturing company uses to make the sleep of the user much more better. ![]()
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